Summary of Deep Spectral Clustering Via Joint Spectral Embedding and Kmeans, by Wengang Guo and Wei Ye
Deep Spectral Clustering via Joint Spectral Embedding and Kmeans
by Wengang Guo, Wei Ye
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning-based approach to spectral clustering is proposed, which addresses the limitations of traditional methods by jointly optimizing spectral embeddings and clustering. The Deep Spectral Clustering (DSC) framework consists of two modules: a spectral embedding module that learns to efficiently embed raw samples using deep neural networks and power iteration, and a greedy Kmeans module that improves cluster structures on learned embeddings through iterative optimization. Experimental results on seven real-world datasets demonstrate the state-of-the-art performance of DSC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spectral clustering is a way to group similar things together. Current methods have some problems, like not working well with high-dimensional data and not being able to optimize everything at once. To fix these issues, scientists created a new approach called Deep Spectral Clustering (DSC). It has two parts: one that learns to represent data in a special way using deep learning, and another that groups the data into clusters based on those representations. They tested DSC on several real-world datasets and found it performed better than other methods. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Embedding » Optimization » Spectral clustering
